Overview

Dataset statistics

Number of variables13
Number of observations2970
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.8 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 3 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qtde_products and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 1 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
qtde_products is highly correlated with qtde_invoicesHigh correlation
avg_ticket is highly correlated with qtde_returns and 1 other fieldsHigh correlation
qtde_returns is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 2 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with qtde_itemsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 5 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_returns is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.45321988) Skewed
qtde_returns is highly skewed (γ1 = 51.80645659) Skewed
avg_basket_size is highly skewed (γ1 = 44.68007213) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2021-12-24 03:46:15.100409
Analysis finished2021-12-24 03:47:03.950377
Duration48.85 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2970
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.86532
Minimum0
Maximum5716
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:04.113787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.45
Q1929.25
median2120.5
Q33537.75
95-th percentile5036.1
Maximum5716
Range5716
Interquartile range (IQR)2608.5

Descriptive statistics

Standard deviation1555.136615
Coefficient of variation (CV)0.6709348474
Kurtosis-1.010761417
Mean2317.86532
Median Absolute Deviation (MAD)1271
Skewness0.3420323534
Sum6884060
Variance2418449.89
MonotonicityStrictly increasing
2021-12-23T23:47:04.381243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30111
 
< 0.1%
29961
 
< 0.1%
29991
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30051
 
< 0.1%
30071
 
< 0.1%
30081
 
< 0.1%
Other values (2960)2960
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57161
< 0.1%
56971
< 0.1%
56871
< 0.1%
56811
< 0.1%
56601
< 0.1%
56561
< 0.1%
56501
< 0.1%
56391
< 0.1%
56381
< 0.1%
56281
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2970
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.71818
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:04.639696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.45
Q113799.75
median15220.5
Q316767.75
95-th percentile17964.55
Maximum18287
Range5940
Interquartile range (IQR)2968

Descriptive statistics

Standard deviation1718.703373
Coefficient of variation (CV)0.112548955
Kurtosis-1.205496241
Mean15270.71818
Median Absolute Deviation (MAD)1487
Skewness0.03170847426
Sum45354033
Variance2953941.285
MonotonicityNot monotonic
2021-12-23T23:47:05.199586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2960)2960
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2955
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2748.786333
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:05.476101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.8075
Q1571.02
median1088.53
Q32307.675
95-th percentile7217.565
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.655

Descriptive statistics

Standard deviation10578.88154
Coefficient of variation (CV)3.848564516
Kurtosis354.0627168
Mean2748.786333
Median Absolute Deviation (MAD)673.06
Skewness16.7803378
Sum8163895.41
Variance111912734.7
MonotonicityNot monotonic
2021-12-23T23:47:05.652835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.962
 
0.1%
533.332
 
0.1%
889.932
 
0.1%
2053.022
 
0.1%
745.062
 
0.1%
2092.322
 
0.1%
379.652
 
0.1%
731.92
 
0.1%
1353.742
 
0.1%
3312
 
0.1%
Other values (2945)2950
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.31346801
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:05.859399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.7564254
Coefficient of variation (CV)1.20902243
Kurtosis2.774045758
Mean64.31346801
Median Absolute Deviation (MAD)26
Skewness1.797124381
Sum191011
Variance6046.061691
MonotonicityNot monotonic
2021-12-23T23:47:06.063078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2220
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.722222222
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:06.320781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.855180674
Coefficient of variation (CV)1.547507302
Kurtosis190.8917484
Mean5.722222222
Median Absolute Deviation (MAD)2
Skewness10.7684025
Sum16995
Variance78.41422477
MonotonicityNot monotonic
2021-12-23T23:47:06.578051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2785
26.4%
3500
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2785
26.4%
3500
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.475758
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:06.880482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.45
Q1296.25
median640
Q31400.5
95-th percentile4407.2
Maximum196844
Range196843
Interquartile range (IQR)1104.25

Descriptive statistics

Standard deviation5886.622254
Coefficient of variation (CV)3.659751927
Kurtosis466.1489505
Mean1608.475758
Median Absolute Deviation (MAD)421
Skewness17.86145938
Sum4777173
Variance34652321.56
MonotonicityNot monotonic
2021-12-23T23:47:07.167044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1509
 
0.3%
889
 
0.3%
2468
 
0.3%
2888
 
0.3%
2728
 
0.3%
848
 
0.3%
2608
 
0.3%
1147
 
0.2%
5167
 
0.2%
Other values (1661)2887
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7020202
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:07.431732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8536468
Coefficient of variation (CV)2.199260015
Kurtosis354.9723233
Mean122.7020202
Median Absolute Deviation (MAD)44
Skewness15.71004775
Sum364425
Variance72820.99067
MonotonicityNot monotonic
2021-12-23T23:47:07.719684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2843
 
1.4%
2037
 
1.2%
3535
 
1.2%
2935
 
1.2%
1934
 
1.1%
1533
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2530
 
1.0%
Other values (458)2630
88.6%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
316
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
926
0.9%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2967
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.88713523
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:08.043192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.917434212
Q113.11982258
median17.96548505
Q324.98612169
95-th percentile90.49558333
Maximum56157.5
Range56155.34941
Interquartile range (IQR)11.86629911

Descriptive statistics

Standard deviation1036.759927
Coefficient of variation (CV)19.98105932
Kurtosis2891.680514
Mean51.88713523
Median Absolute Deviation (MAD)5.975979236
Skewness53.45321988
Sum154104.7916
Variance1074871.146
MonotonicityNot monotonic
2021-12-23T23:47:08.326943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152
 
0.1%
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
31.54080461
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
21.474358971
 
< 0.1%
Other values (2957)2957
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.33548719
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:08.574805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.92445055
median48.26785714
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.40888278

Descriptive statistics

Standard deviation63.53819113
Coefficient of variation (CV)0.9436063178
Kurtosis4.889533464
Mean67.33548719
Median Absolute Deviation (MAD)26.26785714
Skewness2.063271059
Sum199986.397
Variance4037.101731
MonotonicityNot monotonic
2021-12-23T23:47:08.738004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
4617
 
0.6%
2117
 
0.6%
1117
 
0.6%
2816
 
0.5%
Other values (1248)2778
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1350
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06327225268
Minimum0.005449591281
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:08.919105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.009433962264
Q10.01777777778
median0.02941176471
Q30.05539964304
95-th percentile0.2222222222
Maximum3
Range2.994550409
Interquartile range (IQR)0.03762186526

Descriptive statistics

Standard deviation0.1344597893
Coefficient of variation (CV)2.125098816
Kurtosis121.5995649
Mean0.06327225268
Median Absolute Deviation (MAD)0.01433823529
Skewness8.774791213
Sum187.9185905
Variance0.01807943493
MonotonicityNot monotonic
2021-12-23T23:47:09.159204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.166666666721
 
0.7%
0.333333333321
 
0.7%
0.0277777777820
 
0.7%
0.0909090909119
 
0.6%
0.062517
 
0.6%
0.133333333316
 
0.5%
0.416
 
0.5%
0.2515
 
0.5%
0.0238095238115
 
0.5%
0.0357142857115
 
0.5%
Other values (1340)2795
94.1%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055096418731
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
31
 
< 0.1%
21
 
< 0.1%
1.5714285711
 
< 0.1%
1.53
 
0.1%
114
0.5%
0.83333333331
 
< 0.1%
0.751
 
< 0.1%
0.666666666712
0.4%
0.65147453081
 
< 0.1%
0.61
 
< 0.1%

qtde_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.13670034
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:09.656241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.55
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.241801
Coefficient of variation (CV)24.33733676
Kurtosis2766.459377
Mean62.13670034
Median Absolute Deviation (MAD)1
Skewness51.80645659
Sum184546
Variance2286875.265
MonotonicityNot monotonic
2021-12-23T23:47:09.965446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2149
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
843
 
1.4%
743
 
1.4%
Other values (204)706
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2149
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1980
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.7846461
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:10.232056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2625
median172.2916667
Q3281.6442308
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.3817308

Descriptive statistics

Standard deviation791.4234658
Coefficient of variation (CV)3.168423192
Kurtosis2256.28608
Mean249.7846461
Median Absolute Deviation (MAD)83.04166667
Skewness44.68007213
Sum741860.399
Variance626351.1022
MonotonicityNot monotonic
2021-12-23T23:47:10.524293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
869
 
0.3%
739
 
0.3%
829
 
0.3%
608
 
0.3%
888
 
0.3%
1368
 
0.3%
758
 
0.3%
1297
 
0.2%
Other values (1970)2883
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.48420374
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-12-23T23:47:10.797189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.666666667
median13.6
Q322.10714286
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.44047619

Descriptive statistics

Standard deviation15.45771808
Coefficient of variation (CV)0.8840961992
Kurtosis29.32853391
Mean17.48420374
Median Absolute Deviation (MAD)6.6
Skewness3.436505023
Sum51928.08512
Variance238.9410481
MonotonicityNot monotonic
2021-12-23T23:47:11.087892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1342
 
1.4%
941
 
1.4%
839
 
1.3%
1639
 
1.3%
1438
 
1.3%
1738
 
1.3%
536
 
1.2%
1136
 
1.2%
736
 
1.2%
1535
 
1.2%
Other values (896)2590
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%
1041
< 0.1%
1011
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%

Interactions

2021-12-23T23:47:00.619147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:16.612755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:20.699145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:24.454682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:28.493493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:32.326152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:35.868437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:40.052504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:43.728123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:48.405912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:51.540949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:54.538307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:57.624553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:00.851699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:16.958738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:20.979163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:24.722391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:28.793864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:32.602716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:36.198077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:40.281157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:44.024212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:48.651784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:51.789375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:54.758497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:57.857377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:01.081795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:17.400347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:21.283564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:25.028848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:29.083180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:32.832641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:36.757622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:40.543168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:44.329577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:48.863033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:52.009135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:54.973548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:58.072033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:01.294681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:17.650126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:21.572136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:25.253114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:29.350440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:33.134447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:37.040725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:40.865818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:44.843821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:49.064996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:52.201862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:55.199548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:58.341959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:01.500777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:17.883490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:21.878415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:25.494813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:29.610587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:33.461499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:37.273958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:41.170831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:45.258637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:49.274378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:52.420486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:55.478211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:58.552433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:01.701091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:18.166012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:22.131983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:25.834973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:29.822561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:33.731907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:37.546019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:41.448822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:45.556996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:49.494393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:52.804292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:55.687071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:58.780711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:01.977241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:18.470614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:22.377406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:26.156152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:30.128035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:34.019053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:37.860095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:41.703668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:46.000846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:49.769329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:53.054971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:55.919373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:59.217110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:02.194147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:18.792474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:22.707259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:26.469787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:30.430848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:34.270390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:38.170201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:41.964050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:46.277356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:49.991664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:53.290383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:56.200025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:59.480345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:02.399740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:19.059138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:22.984926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:26.725406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:30.725690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:34.536016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:38.525165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:42.299072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:46.669846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:50.359382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:53.491296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:56.415401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:59.644776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:02.597872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:19.322541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:23.301814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:27.003165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:31.046187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:34.829833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:38.802190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:42.607992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:47.107989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:50.593053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:53.741736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:56.653349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:59.813576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:02.791770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:19.867088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:23.578607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:27.346636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:31.304849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:35.099458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:39.081727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:42.917709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:47.398498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:50.821275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:53.949774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:56.865541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:59.988126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:03.001230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:20.149274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:23.826189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:27.691845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:31.699528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:35.377384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:39.405463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:43.169460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:47.681624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:51.088566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:54.120162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:57.159529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:00.201061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:03.215631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:20.430374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:24.152241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:28.033039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:32.036378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:35.635928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:39.757243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:43.425425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:48.094610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:51.321557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:54.297841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:46:57.367667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-23T23:47:00.395266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2021-12-23T23:47:11.314556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-23T23:47:11.614174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-23T23:47:11.890152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-23T23:47:12.192909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-23T23:47:03.471514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-23T23:47:03.808255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.5000000.48611140.050.9705880.617647
11130473232.5956.09.01390.0171.018.90403527.2500000.04878035.0154.44444411.666667
22125836705.382.015.05028.0232.028.90250023.1875000.04569950.0335.2000007.600000
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000004.800000
4415100876.00333.03.080.03.0292.0000008.6000000.13636422.026.6666670.333333
55152914623.3025.014.02102.0102.045.32647123.2000000.05444129.0150.1428574.357143
66146885630.877.021.03621.0327.017.21978618.3000000.073569399.0172.4285717.047619
77178095411.9116.012.02057.061.088.71983635.7000000.03910641.0171.4166673.833333
881531160767.900.091.038194.02379.025.5434644.1444440.315508474.0419.7142866.230769
99160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29605628177271060.2515.01.0645.066.016.0643946.00.2857146.0645.00000066.000000
2961563817232421.522.02.0203.036.011.70888912.00.1538460.0101.50000015.000000
2962563917468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.500000
2963565013596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000066.500000
29645656148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.000000
2965566012479473.2011.01.0382.030.015.7733334.00.33333334.0382.00000030.000000
2966568114126706.137.03.0508.015.047.0753333.01.00000050.0169.3333334.666667
29675687135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333104.000000
2968569715060301.848.04.0262.0120.02.5153331.02.0000000.065.50000020.000000
2969571612558269.967.01.0196.011.024.5418186.00.285714196.0196.00000011.000000